AigeoRadar

AigeoRadar · AI Context Benchmark

AI Context Benchmark

Question → sufficient layer(s) → retrieved slice → measured cost → evidence

WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

https://aigeoradar.com/blog/wordpress-geo-llms-txt-guide

20 questions Answer LLM: gpt-4o-mini ai_context_benchmark_v1

Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 4 · Schema 9 · AIPM 4 · multi-layer 0 · unanswered 3. Descriptive counts only; no overall ranking.

Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 4 · Schema 9 · AIPM 4 · multi-layer 0 · unanswered 3. Descriptive counts only; no overall ranking. Each question shows: sufficient layer(s) → retrieved slice → measured input tokens → evidence. Readers interpret; the report does not rank formats. Semantic Redundancy 0% — fields are relatively distinct.

Gold mode · Independent HTML gold

Gold answers are derived from the HTML page (title, h1, lang, prose) — not from AIPM fields. Understanding/Retrieval therefore measure layer capability, not AIPM self-consistency. Human-authored gold.json remains the gold standard for publication-grade claims.

Execution provenance

Methodology
AI Context Benchmark Methodology v1.0
Planner Spec
v1.0
Execution Protocol
v1.0
Question pack
universal_v1
Score version
aipm_benchmark_score_v10
Engine
aipm_benchmark_v4
Answer LLM
openai / gpt-4o-mini (single provider this lab)
Model snapshot
2026-07
Run id
fb41fe06-5b2a-4e13-868a-ce5c3cdb3e02

Question outcomes

Per question: which layers were sufficient, and what was the lowest measured retrieval cost among them. No overall winner.

4

Lowest cost: HTML

9

Lowest cost: Schema

4

Lowest cost: AIPM

0

Multi-layer

3

Unanswered

Manifest design

Semantic Redundancy · 0%

Semantic Redundancy 0% — fields are relatively distinct.

  • →Semantic fields look reasonably distinct — keep orientation fields lean and non-duplicative.

Structural size (secondary)

Structural size is secondary. A larger AIPM is not a failure if per-question retrieval stays tiny — check Semantic Redundancy instead.

HTML · 23,833 chars
Schema · 14,231 chars
AIPM · 17,159 chars

Routing helper (secondary)

Illustrative card-first vs HTML-always simulation — prefer per-question measured cost above. Not a ranking.

Card-first retrieval would use ~22% fewer tokens than HTML-always on this pack (13 answered from machine card, 7 escalated to HTML). Descriptive only.

Orientation pack

10 questions · all layers scored

  • HTML 8/10
  • Schema 3/10
  • AIPM 6/10

Depth pack

10 questions · all layers scored

  • HTML 5/10
  • Schema 7/10
  • AIPM 7/10

Full-context pack metrics (secondary)

These measure the whole file fed to the model this run — not the minimum slice needed per question.

HTML

13/20 matched

11,634 full-pack tokens

Schema

10/20 matched

8,875 full-pack tokens

AIPM

13/20 matched

8,756 full-pack tokens

Full-pack resource table (secondary)

Whole-file context fed this run. Prefer Minimal Retrieval Cost on each question.

Metric HTML Schema AIPM
Coverage (matched) 13/20 10/20 13/20
Context size 23,833 chars 14,231 chars 17,159 chars
Total tokens 11,634 8,875 8,756
Tokens / correct answer 895 888 674
Est. cost / correct answer $0.000147 $0.000145 $0.000110
Matched per 1k tokens 1.117 1.127 1.485
Median latency 1,546 ms 1,252 ms 1,212 ms
Est. cost (USD) $0.00192 $0.00145 $0.00143

Six independent scores

Answer Efficiency is the primary cost lens. Accuracy axes remain for research — no combined total or winner.

Answer Efficiency

Matched answers per 1k tokens (and cost per match). The primary efficiency axis — not raw accuracy.

  • HTML 75.2
  • Schema 75.9
  • AIPM 100

Understanding

Can this layer convey what the page is about — using independent HTML gold?

  • HTML 85.7
  • Schema 28.6
  • AIPM 57.1

Retrieval

Can this layer surface shared facts (location, contact, hours, pricing, FAQ, CTA)?

  • HTML 55.6
  • Schema 77.8
  • AIPM 77.8

Evidence

Answer quality vs independent gold (score strength). Partial credit counts; UNKNOWN scores zero unless gold is UNKNOWN.

  • HTML 57.3
  • Schema 53.2
  • AIPM 67.9

Metadata

Language, page kind, and freshness from page signals.

  • HTML 66.7
  • Schema 33.3
  • AIPM 66.7

Compression

Information delivered per token and context size. Higher means more matched answers for less context cost.

  • HTML 75.2
  • Schema 75.9
  • AIPM 100

Coverage map

AIPM matched 13 question(s) (alone on Q5, Q20); HTML matched 13. HTML/Schema (or a gap) still needed on Q1, Q6, Q7, Q15, Q16, Q18, Q19. No layer matched gold on Q15, Q16, Q18. Read this as complementary coverage — AIPM orients agents cheaply; HTML supplies depth when the sidecar cannot. HTML 13/20 matched (≈895 tok/match). Schema 10/20 matched (≈888 tok/match). AIPM 13/20 matched (≈674 tok/match). Figures are descriptive per layer — not a ranking. See per-question chains for sufficient layers, slice size, and measured cost.

AIPM matched

Q2, Q3, Q4, Q5, Q8, Q9, Q10, Q11, Q12, Q13, Q14, Q17, Q20

Alone: Q5, Q20

AIPM insufficient

Q1, Q6, Q7, Q15, Q16, Q18, Q19

HTML/Schema needed or all layers missed

Unanswered by all

Q15, Q16, Q18

HTML layer

Visible page text after stripping AIPM sidecars and JSON-LD. Measures what prose alone can answer.

Context fed: 23,833 chars

Tokens: 11,634 · median 1,546 ms

Matched this pack: 13/20

Stronger on

Understanding (85.7) · Compression (75.2) · Answer Efficiency (75.2)

Weaker on

—

Schema layer

JSON-LD structured data with minimal page chrome. Measures what schema markup can answer.

Context fed: 14,231 chars

Tokens: 8,875 · median 1,252 ms

Matched this pack: 10/20

Stronger on

Retrieval (77.8) · Compression (75.9) · Answer Efficiency (75.9)

Weaker on

Understanding (28.6) · Metadata (33.3)

AIPM layer

AI Page Manifest (.ai.json) only. Measures what the machine layer can answer without HTML.

Context fed: 17,159 chars

Tokens: 8,756 · median 1,212 ms

Matched this pack: 13/20

Stronger on

Retrieval (77.8) · Compression (100) · Answer Efficiency (100)

Weaker on

—

When to use which layer

AIPM complements HTML — it does not replace full-page prose.

Scenario Recommended Why
Fast orientation (title, purpose, brand, intent) Compare machine card → HTML fallback on this run Machine-card pack: 8,756 tok · $0.00143. Routing sim saved ~22% tokens vs HTML-always.
Deep content / research (prose facts, process detail) HTML (with optional machine orientation) HTML pack: 11,634 tok · $0.00192. Use when depth needs body prose.
Structured entity pulls (org, location, typed fields) Schema.org Schema pack: 8,875 tok · $0.00145. Dense JSON-LD tends to score well here.

Findings

  • —Of 20 questions: lowest measured retrieval cost among sufficient layers — HTML 4 · Schema 9 · AIPM 4 · multi-layer 0 · unanswered 3. Descriptive counts only; no overall ranking.
  • —Semantic Redundancy 0% — fields are relatively distinct.
  • —Manifest design: Semantic fields look reasonably distinct — keep orientation fields lean and non-duplicative.
  • —Structural size is secondary. A larger AIPM is not a failure if per-question retrieval stays tiny — check Semantic Redundancy instead.
  • —AIPM matched 13 question(s) (alone on Q5, Q20); HTML matched 13. HTML/Schema (or a gap) still needed on Q1, Q6, Q7, Q15, Q16, Q18, Q19. No layer matched gold on Q15, Q16, Q18. Read this as complementary coverage — AIPM orients agents cheaply; HTML supplies depth when the sidecar cannot. HTML 13/20 matched (≈895 tok/match). Schema 10/20 matched (≈888 tok/match). AIPM 13/20 matched (≈674 tok/match). Figures are descriptive per layer — not a ranking. See per-question chains for sufficient layers, slice size, and measured cost.

Question-by-question layer analysis

Which layer(s) could answer; which need more or different context; minimum context fed this run.

Q1 · Understanding · orientation

What is this page or business?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|title|jsonld.name · html_independent

Sufficient: HTML · Partial: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 493 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title+h1+meta_description+lead_paragraphs+prose_span+body_excerpt 584 api 2 medium yes
Schema name+headline+description 493 api 2 medium no
AIPM title+primaryTopic+purpose+abstract+publisher+keyFacts+audience+contentIntent 495 api 2 medium no
Execution chain · AIPM · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on AIPM to minimal fields: title, primaryTopic.
  3. Slice: title+primaryTopic+purpose+abstract+publisher+keyFacts+audience+contentIntent — AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar AIPM.primaryTopic: WordPres...
  4. Actual input tokens: 495
  5. LLM answer: This page is about WordPress GEO publishing and how it helps businesses become findable by AI assistants.
  6. Evidence: AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar AIPM.primaryTopic: WordPress GEO: llms.txt, llms-full.txt, and AI d...
  7. Confidence: medium · matched no

Round 1: 138 in-tok · score 0 · miss

Round 2: 357 in-tok · score 50 · miss

Execution chain · HTML · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on HTML to minimal fields: title, h1.
  3. Slice: title+h1+meta_description+lead_paragraphs+prose_span+body_excerpt — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.h1: WordP...
  4. Actual input tokens: 584
  5. LLM answer: This page is a guide for WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy feature.
  6. Evidence: HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and A...
  7. Confidence: medium · matched yes

Round 1: 143 in-tok · score 0 · miss

Round 2: 441 in-tok · score 58 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What is this page or business?
  2. Planner: Planner mapped question `entity_what` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 493
  5. LLM answer: This page explains the WordPress GEO publishing workflow and related topics.
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: medium · matched no

Round 1: 168 in-tok · score 50 · miss

Round 2: 325 in-tok · score 23 · miss

Root cause · Partial semantic match

Tighten primaryTopic/purpose/abstract so wording overlaps independent HTML gold.

HTML

sufficient

This page is a guide for WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy feature.

score 58 · 23,833 chars context · 584 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

partial

This page explains the WordPress GEO publishing workflow and related topics.

score 50 · 14,231 chars context · 493 in-tokens

Schema was only partially aligned with gold from 14231 chars of context.

AIPM

partial

This page is about WordPress GEO publishing and how it helps businesses become findable by AI assistants.

score 50 · 17,159 chars context · 495 in-tokens

AIPM was only partially aligned with gold from 17159 chars of context.

Q2 · Understanding · orientation

What is the main service or product offered?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|meta|prose · html_independent

Sufficient: HTML, AIPM · Needs more / other context: Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 174 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML h1+title+meta_description+lead_paragraphs+prose_span+body_excerpt 588 api 2 medium yes
Schema name+headline+description+publisher+@type+block 497 api 2 low no
AIPM primaryTopic+title+purpose 174 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on AIPM to minimal fields: primaryTopic, title, purpose.
  3. Slice: primaryTopic+title+purpose — AIPM.primaryTopic: WordPress GEO: llms.txt, llms-full.txt, and AI discovery AIPM.title: WordPress G...
  4. Actual input tokens: 174
  5. LLM answer: The main service offered is guidance for WordPress site owners on the importance of GEO discovery files for AI findability.
  6. Evidence: AIPM.primaryTopic: WordPress GEO: llms.txt, llms-full.txt, and AI discovery AIPM.title: WordPress GEO: llms.txt and AI Discovery Guide | Ai...
  7. Confidence: high · matched yes
Execution chain · HTML · 2 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on HTML to minimal fields: h1, title.
  3. Slice: h1+title+meta_description+lead_paragraphs+prose_span+body_excerpt — HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO...
  4. Actual input tokens: 588
  5. LLM answer: The main service offered is a guide for WordPress owners to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy.
  6. Evidence: HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | Aige...
  7. Confidence: medium · matched yes

Round 1: 145 in-tok · score 0 · miss

Round 2: 443 in-tok · score 58 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What is the main service or product offered?
  2. Planner: Planner mapped question `main_service` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description+publisher+@type+block — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 497
  5. LLM answer: AigeoRadar offers an AI visibility and GEO (Generative Engine Optimization) platform that crawls websites and generates fixes.
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: low · matched no

Round 1: 170 in-tok · score 0 · miss

Round 2: 327 in-tok · score 23 · miss

HTML

sufficient

The main service offered is a guide for WordPress owners to publish llms.txt and related AI discovery files using AigeoRadar's one-click deploy.

score 58 · 23,833 chars context · 588 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

insufficient

AigeoRadar offers an AI visibility and GEO (Generative Engine Optimization) platform that crawls websites and generates fixes.

score 23 · 14,231 chars context · 497 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

sufficient

The main service offered is guidance for WordPress site owners on the importance of GEO discovery files for AI findability.

score 100 · 17,159 chars context · 174 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q3 · Understanding · orientation

Who is the publisher or brand?

Gold: AigeoRadar

Gold source: html.jsonld|title · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost AIPM · 103 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title 118 api 1 high yes
Schema publisher+name 111 api 1 high yes
AIPM publisher 103 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on AIPM to minimal fields: publisher.
  3. Slice: publisher — AIPM.publisher: AigeoRadar
  4. Actual input tokens: 103
  5. LLM answer: AigeoRadar
  6. Evidence: AigeoRadar
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on HTML to minimal fields: title.
  3. Slice: title — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar
  4. Actual input tokens: 118
  5. LLM answer: AigeoRadar.
  6. Evidence: HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: Who is the publisher or brand?
  2. Planner: Planner mapped question `publisher` on SCHEMA to minimal fields: publisher, name.
  3. Slice: publisher+name — SCHEMA.publisher: AigeoRadar SCHEMA.name: AigeoRadar
  4. Actual input tokens: 111
  5. LLM answer: AigeoRadar.
  6. Evidence: SCHEMA.publisher: AigeoRadar SCHEMA.name: AigeoRadar
  7. Confidence: high · matched yes

HTML

sufficient

AigeoRadar.

score 100 · 23,833 chars context · 118 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

AigeoRadar.

score 100 · 14,231 chars context · 111 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

AigeoRadar

score 100 · 17,159 chars context · 103 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q4 · Retrieval · depth

Where does this business operate or which city is mentioned?

Gold: UNKNOWN

Gold source: html.address|prose|jsonld · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 290 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt 606 api 2 low yes
Schema name+headline+description+publisher+@type+block 290 api 2 low yes
AIPM purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent 361 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on AIPM to minimal fields: geo, areaServed, address.
  3. Slice: purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent — AIPM.purpose: Help WordPress site owners understand why GEO discovery files are required after SEO,...
  4. Actual input tokens: 361
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 361 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on HTML to minimal fields: prose_span, lead_paragraphs.
  3. Slice: prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt — HTML.prose_span: UNKNOWN HTML.lead_paragraphs: This guide shows WordPress owners how to publish llm...
  4. Actual input tokens: 606
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 180 in-tok · score 100 · match

Round 2: 426 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: Where does this business operate or which city is mentioned?
  2. Planner: Planner mapped question `geo_signal` on SCHEMA to minimal fields: address, addressLocality, areaServed.
  3. Slice: name+headline+description+publisher+@type+block — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 290
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 290 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 23,833 chars context · 606 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 290 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 361 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q5 · Understanding · orientation

Who is the intended audience of this page?

Gold: businesses

Gold source: html.prose|meta · html_independent

Sufficient: AIPM · Needs more / other context: HTML, Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 165 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt 634 api 2 low no
Schema description+name+headline+publisher+@type+block 475 api 2 low no
AIPM audience+purpose 165 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on AIPM to minimal fields: audience, purpose.
  3. Slice: audience+purpose — AIPM.audience: ["businesses","website owners","WordPress site owners","SEO professionals","digital a...
  4. Actual input tokens: 165
  5. LLM answer: The intended audience of this page includes businesses, website owners, WordPress site owners, SEO professionals, digital agencies, B2B, SMBs, agency, and SMB.
  6. Evidence: AIPM.audience: ["businesses","website owners","WordPress site owners","SEO professionals","digital agencies","B2B","SMBs","agency","SMB"] A...
  7. Confidence: high · matched yes
Execution chain · HTML · 2 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 634
  5. LLM answer: The intended audience of this page is WordPress owners.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: low · matched no

Round 1: 209 in-tok · score 0 · miss

Round 2: 425 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: Who is the intended audience of this page?
  2. Planner: Planner mapped question `audience` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name+headline+publisher+@type+block — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 475
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 148 in-tok · score 0 · miss

Round 2: 327 in-tok · score 0 · miss

HTML

insufficient

The intended audience of this page is WordPress owners.

score 0 · 23,833 chars context · 634 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 475 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

sufficient

The intended audience of this page includes businesses, website owners, WordPress site owners, SEO professionals, digital agencies, B2B, SMBs, agency, and SMB.

score 88 · 17,159 chars context · 165 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q6 · Understanding · orientation

What is the purpose of this page?

Gold: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Gemini visibility.

Gold source: html.page_summary · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 434 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt 663 api 2 medium yes
Schema description+name+headline+publisher+@type+block 434 api 2 low no
AIPM purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent 486 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on AIPM to minimal fields: purpose.
  3. Slice: purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent — AIPM.purpose: Help WordPress site owners understand why GEO discovery files are required after SEO,...
  4. Actual input tokens: 486
  5. LLM answer: The purpose of this page is to help WordPress site owners understand why GEO discovery files are required after SEO and how publishing them makes a business fin...
  6. Evidence: AIPM.purpose: Help WordPress site owners understand why GEO discovery files are required after SEO, and how publishing them makes a business...
  7. Confidence: low · matched no

Round 1: 128 in-tok · score 13 · miss

Round 2: 358 in-tok · score 13 · miss

Execution chain · HTML · 2 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 663
  5. LLM answer: The purpose of this page is to guide WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: medium · matched yes

Round 1: 208 in-tok · score 53 · match

Round 2: 455 in-tok · score 70 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What is the purpose of this page?
  2. Planner: Planner mapped question `purpose` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name+headline+publisher+@type+block — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 434
  5. LLM answer: The purpose of this page is to explain the WordPress GEO publishing workflow and related topics.
  6. Evidence: SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after c...
  7. Confidence: low · matched no

Round 1: 147 in-tok · score 19 · miss

Round 2: 287 in-tok · score 19 · miss

Root cause · Layer miss

Improve orientation fields; do not paste full HTML into the sidecar.

HTML

sufficient

The purpose of this page is to guide WordPress owners on how to publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.

score 70 · 23,833 chars context · 663 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

insufficient

The purpose of this page is to explain the WordPress GEO publishing workflow and related topics.

score 19 · 14,231 chars context · 434 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

The purpose of this page is to help WordPress site owners understand why GEO discovery files are required after SEO and how publishing them makes a business findable by AI assistants.

score 13 · 17,159 chars context · 486 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q7 · Understanding · orientation

Summarize this page in one sentence.

Gold: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Gemini visibility.

Gold source: html.page_summary · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 209 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs 209 api 1 medium yes
Schema description+name+headline+publisher+@type+block 436 api 2 low no
AIPM abstract+purpose+primaryTopic+title+publisher+keyFacts+audience+contentIntent 533 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on AIPM to minimal fields: abstract, purpose.
  3. Slice: abstract+purpose+primaryTopic+title+publisher+keyFacts+audience+contentIntent — AIPM.abstract: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how C...
  4. Actual input tokens: 533
  5. LLM answer: The page explains the WordPress GEO publishing workflow, the importance of GEO discovery files for AI visibility, and the specific files involved in this proces...
  6. Evidence: AIPM.abstract: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after crawl,...
  7. Confidence: low · matched no

Round 1: 174 in-tok · score 15 · miss

Round 2: 359 in-tok · score 22 · miss

Execution chain · HTML · 1 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 209
  5. LLM answer: This guide explains how WordPress owners can publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.
  6. Evidence: HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GEO deploy for ChatGPT, Perplexity, and Ge...
  7. Confidence: medium · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: Summarize this page in one sentence.
  2. Planner: Planner mapped question `abstract` on SCHEMA to minimal fields: description, name.
  3. Slice: description+name+headline+publisher+@type+block — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 436
  5. LLM answer: The page explains the WordPress GEO publishing workflow, including discovery assets, Connector-based deployment, and the relevance of Shopify GEO versus WordPre...
  6. Evidence: SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how Connector-based deploy works after c...
  7. Confidence: low · matched no

Round 1: 148 in-tok · score 13 · miss

Round 2: 288 in-tok · score 14 · miss

Root cause · Layer miss

Improve orientation fields; do not paste full HTML into the sidecar.

HTML

sufficient

This guide explains how WordPress owners can publish llms.txt and related AI discovery files using AigeoRadar for improved AI visibility.

score 70 · 23,833 chars context · 209 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

insufficient

The page explains the WordPress GEO publishing workflow, including discovery assets, Connector-based deployment, and the relevance of Shopify GEO versus WordPress.

score 14 · 14,231 chars context · 436 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

The page explains the WordPress GEO publishing workflow, the importance of GEO discovery files for AI visibility, and the specific files involved in this process.

score 22 · 17,159 chars context · 533 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q8 · Understanding · orientation

What is the primary topic or headline of this page?

Gold: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026

Gold source: html.h1|title · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 147 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML h1+title 147 api 1 high yes
Schema name+headline+description 172 api 1 medium yes
AIPM primaryTopic+title+purpose 176 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on AIPM to minimal fields: primaryTopic, title, purpose.
  3. Slice: primaryTopic+title+purpose — AIPM.primaryTopic: WordPress GEO: llms.txt, llms-full.txt, and AI discovery AIPM.title: WordPress G...
  4. Actual input tokens: 176
  5. LLM answer: WordPress GEO: llms.txt, llms-full.txt, and AI discovery.
  6. Evidence: WordPress GEO: llms.txt, llms-full.txt, and AI discovery
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on HTML to minimal fields: h1, title.
  3. Slice: h1+title — HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO...
  4. Actual input tokens: 147
  5. LLM answer: The primary topic or headline of this page is "WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026."
  6. Evidence: HTML.h1: WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026 HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | Aige...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: What is the primary topic or headline of this page?
  2. Planner: Planner mapped question `primary_topic` on SCHEMA to minimal fields: name, headline, description.
  3. Slice: name+headline+description — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 172
  5. LLM answer: The primary topic or headline of this page is "WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar."
  6. Evidence: SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar SCHEMA.description: Explains the Word...
  7. Confidence: medium · matched yes

HTML

sufficient

The primary topic or headline of this page is "WordPress GEO: llms.txt, llms-full.txt, and AI discovery in 2026."

score 87 · 23,833 chars context · 147 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

The primary topic or headline of this page is "WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar."

score 69 · 14,231 chars context · 172 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

WordPress GEO: llms.txt, llms-full.txt, and AI discovery.

score 96 · 17,159 chars context · 176 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q9 · Metadata · orientation

What language is this page written in?

Gold: en

Gold source: html.lang · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 100 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lang 100 api 1 high yes
Schema inLanguage 101 api 1 high yes
AIPM inLanguage 102 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on AIPM to minimal fields: inLanguage.
  3. Slice: inLanguage — AIPM.inLanguage: en
  4. Actual input tokens: 102
  5. LLM answer: The page is written in English.
  6. Evidence: AIPM.inLanguage: en
  7. Confidence: high · matched yes
Execution chain · HTML · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on HTML to minimal fields: lang.
  3. Slice: lang — HTML.lang: lang=en
  4. Actual input tokens: 100
  5. LLM answer: English.
  6. Evidence: HTML.lang: lang=en
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 1 round(s)
  1. Question: What language is this page written in?
  2. Planner: Planner mapped question `language` on SCHEMA to minimal fields: inLanguage.
  3. Slice: inLanguage — SCHEMA.inLanguage: en
  4. Actual input tokens: 101
  5. LLM answer: The page is written in English.
  6. Evidence: SCHEMA.inLanguage: en
  7. Confidence: high · matched yes

HTML

sufficient

English.

score 100 · 23,833 chars context · 100 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

The page is written in English.

score 88 · 14,231 chars context · 101 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

The page is written in English.

score 88 · 17,159 chars context · 102 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q10 · Retrieval · depth

What phone number or contact detail is listed?

Gold: UNKNOWN

Gold source: html.title|prose|jsonld.telephone · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 529 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML title+lead_paragraphs+h1+meta_description+prose_span+body_excerpt 620 api 2 low yes
Schema block+description+name+headline+publisher+@type 529 api 2 low yes
AIPM keyFacts+publisher+abstract+purpose+primaryTopic+title+audience+contentIntent 594 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on AIPM to minimal fields: keyFacts, publisher, abstract.
  3. Slice: keyFacts+publisher+abstract+purpose+primaryTopic+title+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 594
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 235 in-tok · score 100 · match

Round 2: 359 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on HTML to minimal fields: title, lead_paragraphs.
  3. Slice: title+lead_paragraphs+h1+meta_description+prose_span+body_excerpt — HTML.title: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRadar — AigeoRadar HTML.lead_para...
  4. Actual input tokens: 620
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 196 in-tok · score 100 · match

Round 2: 424 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What phone number or contact detail is listed?
  2. Planner: Planner mapped question `contact_phone` on SCHEMA to minimal fields: telephone, block, description.
  3. Slice: block+description+name+headline+publisher+@type — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 529
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 241 in-tok · score 100 · match

Round 2: 288 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 23,833 chars context · 620 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 529 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 594 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q11 · Retrieval · depth

What physical address is listed for this business?

Gold: UNKNOWN

Gold source: html.prose|jsonld.address · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 288 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt 602 api 2 low yes
Schema name+headline+description+publisher+@type+block 288 api 2 low yes
AIPM purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent 359 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on AIPM to minimal fields: address, geo, areaServed.
  3. Slice: purpose+abstract+primaryTopic+title+publisher+keyFacts+audience+contentIntent — AIPM.purpose: Help WordPress site owners understand why GEO discovery files are required after SEO,...
  4. Actual input tokens: 359
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 359 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on HTML to minimal fields: prose_span, lead_paragraphs.
  3. Slice: prose_span+lead_paragraphs+title+h1+meta_description+body_excerpt — HTML.prose_span: UNKNOWN HTML.lead_paragraphs: This guide shows WordPress owners how to publish llm...
  4. Actual input tokens: 602
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 178 in-tok · score 100 · match

Round 2: 424 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What physical address is listed for this business?
  2. Planner: Planner mapped question `address` on SCHEMA to minimal fields: address, addressLocality, areaServed.
  3. Slice: name+headline+description+publisher+@type+block — SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI Discovery Guide | AigeoRada...
  4. Actual input tokens: 288
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 0 in-tok · score 0 · miss

Round 2: 288 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 23,833 chars context · 602 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 288 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 359 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q12 · Retrieval · depth

What are the opening hours or availability?

Gold: UNKNOWN

Gold source: html.prose|jsonld.openingHours · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 527 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 758 api 2 low yes
Schema block+description+name+headline+publisher+@type 527 api 2 low yes
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent 616 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 616
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 258 in-tok · score 100 · match

Round 2: 358 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 758
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 335 in-tok · score 100 · match

Round 2: 423 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What are the opening hours or availability?
  2. Planner: Planner mapped question `hours` on SCHEMA to minimal fields: openingHours, block, description.
  3. Slice: block+description+name+headline+publisher+@type — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 527
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 240 in-tok · score 100 · match

Round 2: 287 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 23,833 chars context · 758 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 527 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 616 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q13 · Retrieval · depth

Is pricing or package information mentioned, and what is it?

Gold: UNKNOWN

Gold source: html.prose|jsonld.offers · html_independent

Sufficient: Schema, AIPM · Needs more / other context: HTML

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 535 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 766 api 2 low no
Schema block+description+name+headline+publisher+@type 535 api 2 low yes
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent 590 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on AIPM to minimal fields: keyFacts, sections, abstract.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 590
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 228 in-tok · score 100 · match

Round 2: 362 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 766
  5. LLM answer: Yes, the starter plan is $9.90 per month and includes one WordPress site with plugin sync.
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: low · matched no

Round 1: 339 in-tok · score 0 · miss

Round 2: 427 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: Is pricing or package information mentioned, and what is it?
  2. Planner: Planner mapped question `pricing` on SCHEMA to minimal fields: offers, price, block, description.
  3. Slice: block+description+name+headline+publisher+@type — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 535
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 244 in-tok · score 100 · match

Round 2: 291 in-tok · score 100 · match

HTML

insufficient

Yes, the starter plan is $9.90 per month and includes one WordPress site with plugin sync.

score 0 · 23,833 chars context · 766 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 535 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 590 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q14 · Retrieval · depth

What makes this offer different or stand out?

Gold: UNKNOWN

Gold source: html.prose|meta · html_independent

Sufficient: HTML, Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 538 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 760 api 2 low yes
Schema description+block+name+headline+publisher+@type 538 api 2 low yes
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent 618 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 618
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 259 in-tok · score 100 · match

Round 2: 359 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 760
  5. LLM answer: UNKNOWN
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 336 in-tok · score 100 · match

Round 2: 424 in-tok · score 100 · match

Execution chain · SCHEMA · 2 round(s)
  1. Question: What makes this offer different or stand out?
  2. Planner: Planner mapped question `differentiator` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 538
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 250 in-tok · score 100 · match

Round 2: 288 in-tok · score 100 · match

HTML

sufficient

UNKNOWN

score 100 · 23,833 chars context · 760 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 538 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 618 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q15 · Evidence · depth

What trust signals are present (reviews, certificates, guarantees, years of experience)?

Gold: review

Gold source: html.prose|jsonld · html_independent

No layer provided a sufficient answer from its context alone.

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 552 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 774 api 2 low no
Schema description+block+name+headline+publisher+@type 552 api 2 low no
AIPM keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent 632 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on AIPM to minimal fields: keyFacts, abstract, purpose.
  3. Slice: keyFacts+abstract+purpose+primaryTopic+title+publisher+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 632
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 266 in-tok · score 0 · miss

Round 2: 366 in-tok · score 0 · miss

Execution chain · HTML · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 774
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 343 in-tok · score 0 · miss

Round 2: 431 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What trust signals are present (reviews, certificates, guarantees, years of experience)?
  2. Planner: Planner mapped question `trust` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 552
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 257 in-tok · score 0 · miss

Round 2: 295 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

insufficient

UNKNOWN.

score 0 · 23,833 chars context · 774 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 552 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 17,159 chars context · 632 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q16 · Retrieval · depth

What frequently asked questions or FAQ topics are covered?

Gold: FAQ topics are present on the page

Gold source: html.faq|jsonld.FAQPage · html_independent

No layer provided a sufficient answer from its context alone.

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 531 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 774 api 2 low no
Schema block+description+name+headline+publisher+@type 531 api 2 low no
AIPM faqEntity+keyFacts+purpose+abstract+primaryTopic+title+publisher+audience+contentIntent 557 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on AIPM to minimal fields: faqEntity, keyFacts.
  3. Slice: faqEntity+keyFacts+purpose+abstract+primaryTopic+title+publisher+audience+contentIntent — AIPM.faqEntity: [] AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that...
  4. Actual input tokens: 557
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 189 in-tok · score 0 · miss

Round 2: 368 in-tok · score 0 · miss

Execution chain · HTML · 2 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 774
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 343 in-tok · score 0 · miss

Round 2: 431 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What frequently asked questions or FAQ topics are covered?
  2. Planner: Planner mapped question `faq` on SCHEMA to minimal fields: block, description.
  3. Slice: block+description+name+headline+publisher+@type — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 531
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 242 in-tok · score 0 · miss

Round 2: 289 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

insufficient

UNKNOWN.

score 0 · 23,833 chars context · 774 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 531 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 17,159 chars context · 557 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q17 · Retrieval · depth

What action should the visitor take (call, buy, contact, book)?

Gold: UNKNOWN

Gold source: html.cta|prose · html_independent

Sufficient: Schema, AIPM · Needs more / other context: HTML

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 550 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description 772 api 2 low no
Schema description+block+name+headline+publisher+@type 550 api 2 low yes
AIPM purpose+abstract+keyFacts+primaryTopic+title+publisher+audience+contentIntent 630 api 2 low yes
Execution chain · AIPM · 2 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on AIPM to minimal fields: purpose, abstract, keyFacts.
  3. Slice: purpose+abstract+keyFacts+primaryTopic+title+publisher+audience+contentIntent — AIPM.purpose: Help WordPress site owners understand why GEO discovery files are required after SEO,...
  4. Actual input tokens: 630
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 265 in-tok · score 100 · match

Round 2: 365 in-tok · score 100 · match

Execution chain · HTML · 2 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span+title+h1+meta_description — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 772
  5. LLM answer: Enter your domain on aigeoradar.com for an instant AI visibility preview.
  6. Evidence: Enter your domain on aigeoradar.com for an instant AI visibility preview. No credit card required.
  7. Confidence: low · matched no

Round 1: 342 in-tok · score 0 · miss

Round 2: 430 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What action should the visitor take (call, buy, contact, book)?
  2. Planner: Planner mapped question `cta` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 550
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched yes

Round 1: 256 in-tok · score 100 · match

Round 2: 294 in-tok · score 100 · match

HTML

insufficient

Enter your domain on aigeoradar.com for an instant AI visibility preview.

score 0 · 23,833 chars context · 772 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

sufficient

UNKNOWN.

score 100 · 14,231 chars context · 550 in-tokens

Schema answered using 14231 chars of layer context (minimum fed this run).

AIPM

sufficient

UNKNOWN.

score 100 · 17,159 chars context · 630 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Q18 · Retrieval · depth

What related products, services, or pages are mentioned?

Gold: Features, What is AigeoRadar?, Pricing

Gold source: html.nav|prose · html_independent

No layer provided a sufficient answer from its context alone.

Measured retrieval cost (API tokens) · lowest cost SCHEMA · 542 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 782 api 2 low no
Schema description+block+name+headline+publisher+@type 542 api 2 low no
AIPM keyFacts+primaryTopic+purpose+abstract+title+publisher+audience+contentIntent 566 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on AIPM to minimal fields: sections, keyFacts, primaryTopic.
  3. Slice: keyFacts+primaryTopic+purpose+abstract+title+publisher+audience+contentIntent — AIPM.keyFacts: GEO (Generative Engine Optimization) is the layer after SEO that uses machine-readabl...
  4. Actual input tokens: 566
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 205 in-tok · score 0 · miss

Round 2: 361 in-tok · score 0 · miss

Execution chain · HTML · 2 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 782
  5. LLM answer: AigeoRadar one-click deploy, WordPress plugin, and Shopify app for GEO discovery deploy.
  6. Evidence: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click deploy works.
  7. Confidence: low · matched no

Round 1: 347 in-tok · score 15 · miss

Round 2: 435 in-tok · score 14 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What related products, services, or pages are mentioned?
  2. Planner: Planner mapped question `related` on SCHEMA to minimal fields: description, block, name.
  3. Slice: description+block+name+headline+publisher+@type — SCHEMA.description: Explains the WordPress GEO publishing workflow: which discovery assets to ship,...
  4. Actual input tokens: 542
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 252 in-tok · score 0 · miss

Round 2: 290 in-tok · score 0 · miss

Root cause · Depth gap (expected)

Prose/detail questions belong to HTML — escalate after AIPM orientation.

HTML

insufficient

AigeoRadar one-click deploy, WordPress plugin, and Shopify app for GEO discovery deploy.

score 15 · 23,833 chars context · 782 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 542 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 17,159 chars context · 566 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q19 · Metadata · orientation

When was this page published or last updated?

Gold: 2026-07-30

Gold source: html.time|jsonld.dateModified · html_independent

Sufficient: HTML · Needs more / other context: Schema, AIPM

Measured retrieval cost (API tokens) · lowest cost HTML · 341 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML lead_paragraphs+body_excerpt+prose_span 341 api 1 high yes
Schema block+description+name+headline+publisher+@type 607 api 2 low no
AIPM abstract+keyFacts+title+purpose+primaryTopic+publisher+audience+contentIntent 606 api 2 low no
Execution chain · AIPM · 2 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on AIPM to minimal fields: abstract, keyFacts, title.
  3. Slice: abstract+keyFacts+title+purpose+primaryTopic+publisher+audience+contentIntent — AIPM.abstract: Explains the WordPress GEO publishing workflow: which discovery assets to ship, how C...
  4. Actual input tokens: 606
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 247 in-tok · score 0 · miss

Round 2: 359 in-tok · score 0 · miss

Execution chain · HTML · 1 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on HTML to minimal fields: lead_paragraphs, body_excerpt, prose_span.
  3. Slice: lead_paragraphs+body_excerpt+prose_span — HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI disco...
  4. Actual input tokens: 341
  5. LLM answer: The page was published or last updated on 2026-07-30.
  6. Evidence: HTML.lead_paragraphs: This guide shows WordPress owners how to publish llms.txt and related AI discovery files, and how AigeoRadar one-click...
  7. Confidence: high · matched yes
Execution chain · SCHEMA · 2 round(s)
  1. Question: When was this page published or last updated?
  2. Planner: Planner mapped question `freshness` on SCHEMA to minimal fields: block, description.
  3. Slice: block+description+name+headline+publisher+@type — SCHEMA.block: { "@context": "https://schema.org", "@graph": [ { "@type":...
  4. Actual input tokens: 607
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 280 in-tok · score 0 · miss

Round 2: 327 in-tok · score 0 · miss

Root cause · Missing in manifest

Add or fix the corresponding AIPM field (title, purpose, primaryTopic, publisher, inLanguage).

HTML

sufficient

The page was published or last updated on 2026-07-30.

score 88 · 23,833 chars context · 341 in-tokens

HTML answered using 23833 chars of layer context (minimum fed this run).

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 607 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

insufficient

UNKNOWN.

score 0 · 17,159 chars context · 606 in-tokens

AIPM did not answer from 17159 chars of context — additional or different layer context needed.

Q20 · Metadata · orientation

What kind of page is this (article, product, local service, company page, FAQ, other)?

Gold: product

Gold source: html.heuristic|jsonld.@type · html_independent

Sufficient: AIPM · Needs more / other context: HTML, Schema

Measured retrieval cost (API tokens) · lowest cost AIPM · 124 input tokens

Layer Planner fields Input tokens Rounds Confidence OK?
HTML meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt 657 api 2 low no
Schema @type+name+headline+description+publisher+block 414 api 2 low no
AIPM contentIntent+pageType 124 api 1 high yes
Execution chain · AIPM · 1 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on AIPM to minimal fields: contentIntent, pageType.
  3. Slice: contentIntent+pageType — AIPM.contentIntent: informational AIPM.pageType: blog
  4. Actual input tokens: 124
  5. LLM answer: blog
  6. Evidence: blog
  7. Confidence: high · matched yes
Execution chain · HTML · 2 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on HTML to minimal fields: meta_description, lead_paragraphs.
  3. Slice: meta_description+lead_paragraphs+title+h1+prose_span+body_excerpt — HTML.meta_description: Publish llms.txt and llms-full.txt on WordPress with AigeoRadar. One-click GE...
  4. Actual input tokens: 657
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 221 in-tok · score 0 · miss

Round 2: 436 in-tok · score 0 · miss

Execution chain · SCHEMA · 2 round(s)
  1. Question: What kind of page is this (article, product, local service, company page, FAQ, other)?
  2. Planner: Planner mapped question `page_kind` on SCHEMA to minimal fields: @type.
  3. Slice: @type+name+headline+description+publisher+block — SCHEMA.@type: Organization SCHEMA.name: AigeoRadar SCHEMA.headline: WordPress GEO: llms.txt and AI...
  4. Actual input tokens: 414
  5. LLM answer: UNKNOWN.
  6. Evidence: —
  7. Confidence: low · matched no

Round 1: 114 in-tok · score 0 · miss

Round 2: 300 in-tok · score 0 · miss

HTML

insufficient

UNKNOWN.

score 0 · 23,833 chars context · 657 in-tokens

HTML did not answer from 23833 chars of context — additional or different layer context needed.

Schema

insufficient

UNKNOWN.

score 0 · 14,231 chars context · 414 in-tokens

Schema did not answer from 14231 chars of context — additional or different layer context needed.

AIPM

sufficient

blog

score 100 · 17,159 chars context · 124 in-tokens

AIPM answered using 17159 chars of layer context (minimum fed this run).

Methodology

  • AI Context Benchmark: Planner → Slice → LLM with measured API input tokens.
  • Reports describe sufficient layers, slice size, cost, and evidence — they do not declare a winning format.
  • Layers under test today: HTML, Schema.org, AIPM (extensible to RSS, Markdown, PDF, …).
  • Engine aipm_benchmark_v4 · Thu, Jul 30, 2026 12:34 AM · aipm_benchmark_score_v10

Share

Per-question context chain across layers.

Post on X LinkedIn WhatsApp
About the lab →